@CodeClause Project : Image Caption Generator¶

In [12]:
#!pip install tensorflow

^C
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
import re
import nltk
from nltk.corpus import stopwords
import string
import json
from time import time
import pickle
from tensorflow.keras.applications import VGG16, ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Input, Dense, Dropout, Embedding, LSTM
# from tensorflow.keras.layers.merge import add
from tensorflow.keras.layers import Input, Dense, Dropout, Embedding, LSTM, concatenate
from tensorflow.keras.applications.resnet import ResNet50, preprocess_input, decode_predictions
from tensorflow.keras.layers import add
In [7]:
# Reading the Description file

with open('D:/PROGRAMMING/PYTHON/Data Science & Analysis/Pr_4_Image Caption Generator/flickr8k-sau/Flickr_Data/Flickr_TextData/Flickr8k.token.txt') as filepath:
    captions = filepath.read()
    filepath.close()
In [8]:
captions = captions.split("\n")[:-1]
In [9]:
len(captions)
Out[9]:
40460
In [11]:
# creating a "descriptions" dictionary  where key is 'img_name' and value is list of captions corresponding to that image_file.

descriptions = {}

for ele in captions:
    i_to_c = ele.split("\t")
    img_name = i_to_c[0].split(".")[0]
    cap = i_to_c[1]
    
    if descriptions.get(img_name) == None:
        descriptions[img_name] = []

    descriptions[img_name].append(cap)
In [12]:
descriptions['1000268201_693b08cb0e']
Out[12]:
['A child in a pink dress is climbing up a set of stairs in an entry way .',
 'A girl going into a wooden building .',
 'A little girl climbing into a wooden playhouse .',
 'A little girl climbing the stairs to her playhouse .',
 'A little girl in a pink dress going into a wooden cabin .']
In [13]:
""" 1. lower each word
    2. remove puntuations
    3. remove words less than length 1 """ 

def clean_text(sample):
    sample = sample.lower()
    
    sample = re.sub("[^a-z]+"," ",sample)
    
    sample = sample.split()
    
    sample = [s for s in sample if len(s)>1]
    
    sample = " ".join(sample)
    
    return sample
In [14]:
clean_text("My noghsujf si am m cricket101 &8 mphi*&86%%&??,BY6fajdn 213 q rqu243 boy  32 ewr wO>>J DHD 34  asfb HHGY Gvg HgB   231 123")
Out[14]:
'my noghsujf si am cricket mphi by fajdn rqu boy ewr wo dhd asfb hhgy gvg hgb'
In [15]:
#  modify all the captions i.e - cleaned captions

for key, desc_list in descriptions.items():
    for i in range(len(desc_list)):
        desc_list[i] = clean_text(desc_list[i])
In [16]:
#  clean descriptions

descriptions['1000268201_693b08cb0e']
Out[16]:
['child in pink dress is climbing up set of stairs in an entry way',
 'girl going into wooden building',
 'little girl climbing into wooden playhouse',
 'little girl climbing the stairs to her playhouse',
 'little girl in pink dress going into wooden cabin']
In [18]:
# writing clean description to .txt file

f = open("descriptions.txt","w")
f.write( str(descriptions) )
f.close()
In [20]:
#  reading description file

f = open("descriptions.txt", 'r')
descriptions = f.read()
f.close()

json_acceptable_string = descriptions.replace("'", "\"")
descriptions = json.loads(json_acceptable_string)
In [21]:
# finding the unique vocabulary 

vocabulary = set()

for key in descriptions.keys():
    [vocabulary.update(i.split()) for i in descriptions[key]]

print('Vocabulary Size: %d' % len(vocabulary))

Vocabulary Size: 8424
In [22]:
#  ALl words in description dictionary
all_vocab =  []

for key in descriptions.keys():
    [all_vocab.append(i) for des in descriptions[key] for i in des.split()]

print('Vocabulary Size: %d' % len(all_vocab))
print(all_vocab[:15])

Vocabulary Size: 373837
['child', 'in', 'pink', 'dress', 'is', 'climbing', 'up', 'set', 'of', 'stairs', 'in', 'an', 'entry', 'way', 'girl']
In [23]:
#  count the frequency of each word, sort them and discard the words having frequency lesser than threshold value

import collections


counter= collections.Counter(all_vocab)

dic_ = dict(counter)

threshelod_value = 10

sorted_dic = sorted(dic_.items(), reverse=True, key = lambda x: x[1])
sorted_dic = [x for x in sorted_dic if x[1]>threshelod_value]
all_vocab = [x[0] for x in sorted_dic]
In [24]:
len(all_vocab)
Out[24]:
1845
In [25]:
# TrainImagesFile
f = open("D:/PROGRAMMING/PYTHON/Data Science & Analysis/Pr_4_Image Caption Generator/flickr8k-sau/Flickr_Data/Flickr_TextData/Flickr_8k.trainImages.txt")
train = f.read()
f.close()
In [26]:
train  = [e.split(".")[0] for e in train.split("\n")[:-1]]
In [27]:
# TestImagesFile
f = open("D:/PROGRAMMING/PYTHON/Data Science & Analysis/Pr_4_Image Caption Generator/flickr8k-sau/Flickr_Data/Flickr_TextData/Flickr_8k.testImages.txt")
test = f.read()
f.close()
In [28]:
test  = [e.split(".")[0] for e in test.split("\n")[:-1]]
In [29]:
# create train_descriptions dictionary, which will be similar to earlier one, but having only train samples
# add startseq + endseq 

train_descriptions = {}

for t in train:
    train_descriptions[t] = []
    for cap in descriptions[t]:
        cap_to_append = "startseq " + cap + " endseq"
        train_descriptions[t].append(cap_to_append)
In [30]:
train_descriptions['1000268201_693b08cb0e']
Out[30]:
['startseq child in pink dress is climbing up set of stairs in an entry way endseq',
 'startseq girl going into wooden building endseq',
 'startseq little girl climbing into wooden playhouse endseq',
 'startseq little girl climbing the stairs to her playhouse endseq',
 'startseq little girl in pink dress going into wooden cabin endseq']
In [31]:
model = ResNet50(weights="imagenet", input_shape=(224,224,3))

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
102967424/102967424 [==============================] - 58s 1us/step
In [32]:
model.summary()

Model: "resnet50"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_1[0][0]']                
                                                                                                  
 conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              
                                )                                                                 
                                                                                                  
 conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             
                                )                                                                 
                                                                                                  
 conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               
                                )                                                                 
                                                                                                  
 pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             
                                )                                                                 
                                                                                                  
 pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              
                                                                                                  
 conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             
                                                                                                  
 conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    
                                                                                                  
 conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             
                                                                                                  
 conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    
                                                                                                  
 conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      
                                                                  'conv2_block1_3_bn[0][0]']      
                                                                                                  
 conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       
                                                                                                  
 conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       
                                                                                                  
 conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    
                                                                                                  
 conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    
                                                                                                  
 conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       
                                                                  'conv2_block2_3_bn[0][0]']      
                                                                                                  
 conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       
                                                                                                  
 conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       
                                                                                                  
 conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    
                                                                                                  
 conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    
                                                                                                  
 conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       
                                                                  'conv2_block3_3_bn[0][0]']      
                                                                                                  
 conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       
                                                                                                  
 conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       
                                                                                                  
 conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    
                                                                                                  
 conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       
                                                                                                  
 conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    
                                                                                                  
 conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      
                                                                  'conv3_block1_3_bn[0][0]']      
                                                                                                  
 conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       
                                                                                                  
 conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       
                                                                                                  
 conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    
                                                                                                  
 conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    
                                                                                                  
 conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       
                                                                  'conv3_block2_3_bn[0][0]']      
                                                                                                  
 conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       
                                                                                                  
 conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       
                                                                                                  
 conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    
                                                                                                  
 conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    
                                                                                                  
 conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       
                                                                  'conv3_block3_3_bn[0][0]']      
                                                                                                  
 conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       
                                                                                                  
 conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       
                                                                                                  
 conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    
                                                                                                  
 conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    
                                                                                                  
 conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       
                                                                  'conv3_block4_3_bn[0][0]']      
                                                                                                  
 conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       
                                                                                                  
 conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       
                                                                                                  
 conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    
                                                                                                  
 conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      
                                )                                 'conv4_block1_3_bn[0][0]']      
                                                                                                  
 conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       
                                                                                                  
 conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    
                                                                                                  
 conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       
                                )                                 'conv4_block2_3_bn[0][0]']      
                                                                                                  
 conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       
                                                                                                  
 conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    
                                                                                                  
 conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       
                                )                                 'conv4_block3_3_bn[0][0]']      
                                                                                                  
 conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       
                                                                                                  
 conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    
                                                                                                  
 conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       
                                )                                 'conv4_block4_3_bn[0][0]']      
                                                                                                  
 conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       
                                                                                                  
 conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    
                                                                                                  
 conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       
                                )                                 'conv4_block5_3_bn[0][0]']      
                                                                                                  
 conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       
                                                                                                  
 conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    
                                                                                                  
 conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    
                                )                                                                 
                                                                                                  
 conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       
                                )                                 'conv4_block6_3_bn[0][0]']      
                                                                                                  
 conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       
                                )                                                                 
                                                                                                  
 conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       
                                                                                                  
 conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    
                                                                                                  
 conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       
                                                                                                  
 conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    
                                                                                                  
 conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      
                                                                  'conv5_block1_3_bn[0][0]']      
                                                                                                  
 conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       
                                                                                                  
 conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       
                                                                                                  
 conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    
                                                                                                  
 conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    
                                                                                                  
 conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       
                                                                  'conv5_block2_3_bn[0][0]']      
                                                                                                  
 conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       
                                                                                                  
 conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       
                                                                                                  
 conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    
                                                                                                  
 conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    
                                                                                                  
 conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       
                                                                  'conv5_block3_3_bn[0][0]']      
                                                                                                  
 conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       
                                                                                                  
 avg_pool (GlobalAveragePooling  (None, 2048)        0           ['conv5_block3_out[0][0]']       
 2D)                                                                                              
                                                                                                  
 predictions (Dense)            (None, 1000)         2049000     ['avg_pool[0][0]']               
                                                                                                  
==================================================================================================
Total params: 25,636,712
Trainable params: 25,583,592
Non-trainable params: 53,120
__________________________________________________________________________________________________
In [33]:
# Create a new model, by removing the last layer (output layer of 1000 classes) from the resnet50
model_new = Model(model.input, model.layers[-2].output)
In [34]:
images = "D:/PROGRAMMING/PYTHON/Data Science & Analysis/Pr_4_Image Caption Generator/flickr8k-sau/Flickr_Data/Images/"
In [35]:
def preprocess_image(img):
    img = image.load_img(img, target_size=(224,224))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = preprocess_input(img)
    return img
In [36]:
def encode_image(img):
    img = preprocess_image(img)
    feature_vector = model_new.predict(img)
    feature_vector = feature_vector.reshape(feature_vector.shape[1],)
    return feature_vector
In [37]:
start = time()

encoding_train = {}

for ix, img in enumerate(train):
    
    img = "D:/PROGRAMMING/PYTHON/Data Science & Analysis/Pr_4_Image Caption Generator/flickr8k-sau/Flickr_Data/Images/{}.jpg".format(train[ix])
    encoding_train[img[len(images):]] = encode_image(img)
    
    if ix%100==0:
        print("Encoding image- "+ str(ix))
    
print("Time taken in seconds =", time()-start)

1/1 [==============================] - 2s 2s/step
Encoding image- 0
1/1 [==============================] - 0s 123ms/step
1/1 [==============================] - 0s 115ms/step
1/1 [==============================] - 0s 185ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 108ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 107ms/step
1/1 [==============================] - 0s 163ms/step
1/1 [==============================] - 0s 116ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 114ms/step
1/1 [==============================] - 0s 112ms/step
1/1 [==============================] - 0s 107ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 158ms/step
1/1 [==============================] - 0s 129ms/step
1/1 [==============================] - 0s 120ms/step
1/1 [==============================] - 0s 133ms/step
1/1 [==============================] - 0s 95ms/step
1/1 [==============================] - 0s 115ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 103ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 108ms/step
1/1 [==============================] - 0s 106ms/step
1/1 [==============================] - 0s 105ms/step
1/1 [==============================] - 0s 106ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 134ms/step
1/1 [==============================] - 0s 115ms/step
1/1 [==============================] - 0s 116ms/step
1/1 [==============================] - 0s 112ms/step
1/1 [==============================] - 0s 122ms/step
1/1 [==============================] - 0s 101ms/step
1/1 [==============================] - 0s 100ms/step
1/1 [==============================] - 0s 128ms/step
1/1 [==============================] - 0s 120ms/step
1/1 [==============================] - 0s 144ms/step
1/1 [==============================] - 0s 138ms/step
1/1 [==============================] - 0s 118ms/step
1/1 [==============================] - 0s 109ms/step
1/1 [==============================] - 0s 120ms/step
1/1 [==============================] - 0s 112ms/step
1/1 [==============================] - 0s 117ms/step
1/1 [==============================] - 0s 142ms/step
1/1 [==============================] - 0s 114ms/step
1/1 [==============================] - 0s 112ms/step
1/1 [==============================] - 0s 112ms/step
1/1 [==============================] - 0s 126ms/step
1/1 [==============================] - 0s 115ms/step
1/1 [==============================] - 0s 118ms/step
1/1 [==============================] - 0s 207ms/step
1/1 [==============================] - 0s 168ms/step
1/1 [==============================] - 0s 115ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 99ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 162ms/step
1/1 [==============================] - 0s 106ms/step
1/1 [==============================] - 0s 103ms/step
1/1 [==============================] - 0s 95ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 104ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 143ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 123ms/step
1/1 [==============================] - 0s 197ms/step
1/1 [==============================] - 0s 123ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 131ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 117ms/step
1/1 [==============================] - 0s 108ms/step
1/1 [==============================] - 0s 156ms/step
1/1 [==============================] - 0s 93ms/step
1/1 [==============================] - 0s 152ms/step
1/1 [==============================] - 0s 125ms/step
1/1 [==============================] - 0s 96ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 95ms/step
1/1 [==============================] - 0s 109ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 98ms/step
1/1 [==============================] - 0s 96ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 95ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 116ms/step
1/1 [==============================] - 0s 131ms/step
1/1 [==============================] - 0s 114ms/step
1/1 [==============================] - 0s 88ms/step
Encoding image- 100
1/1 [==============================] - 0s 93ms/step
1/1 [==============================] - 0s 79ms/step
1/1 [==============================] - 0s 95ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 81ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 97ms/step
1/1 [==============================] - 0s 133ms/step
1/1 [==============================] - 0s 125ms/step
1/1 [==============================] - 0s 93ms/step
1/1 [==============================] - 0s 125ms/step
1/1 [==============================] - 0s 96ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 129ms/step
1/1 [==============================] - 0s 88ms/step
1/1 [==============================] - 0s 143ms/step
1/1 [==============================] - 0s 116ms/step
1/1 [==============================] - 0s 99ms/step
1/1 [==============================] - 0s 136ms/step
1/1 [==============================] - 0s 112ms/step
1/1 [==============================] - 0s 140ms/step
1/1 [==============================] - 0s 130ms/step
1/1 [==============================] - 0s 91ms/step
1/1 [==============================] - 0s 137ms/step
1/1 [==============================] - 0s 113ms/step
1/1 [==============================] - 0s 109ms/step
1/1 [==============================] - 0s 140ms/step
1/1 [==============================] - 0s 95ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 132ms/step
1/1 [==============================] - 0s 100ms/step
1/1 [==============================] - 0s 294ms/step
1/1 [==============================] - 0s 321ms/step
1/1 [==============================] - 0s 275ms/step
1/1 [==============================] - 0s 289ms/step
1/1 [==============================] - 0s 258ms/step
1/1 [==============================] - 0s 203ms/step
1/1 [==============================] - 0s 241ms/step
1/1 [==============================] - 0s 168ms/step
1/1 [==============================] - 0s 238ms/step
1/1 [==============================] - 0s 257ms/step
1/1 [==============================] - 0s 182ms/step
1/1 [==============================] - 0s 193ms/step
1/1 [==============================] - 0s 213ms/step
1/1 [==============================] - 0s 175ms/step
1/1 [==============================] - 0s 165ms/step
1/1 [==============================] - 0s 141ms/step
1/1 [==============================] - 0s 156ms/step
1/1 [==============================] - 0s 205ms/step
1/1 [==============================] - 0s 203ms/step
1/1 [==============================] - 0s 166ms/step
1/1 [==============================] - 0s 141ms/step
1/1 [==============================] - 0s 118ms/step
1/1 [==============================] - 0s 125ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 97ms/step
1/1 [==============================] - 0s 108ms/step
1/1 [==============================] - 0s 166ms/step
1/1 [==============================] - 0s 113ms/step
1/1 [==============================] - 0s 158ms/step
1/1 [==============================] - 0s 165ms/step
1/1 [==============================] - 0s 101ms/step
1/1 [==============================] - 0s 117ms/step
1/1 [==============================] - 0s 105ms/step
1/1 [==============================] - 0s 169ms/step
1/1 [==============================] - 0s 116ms/step
1/1 [==============================] - 0s 158ms/step
1/1 [==============================] - 0s 116ms/step
1/1 [==============================] - 0s 132ms/step
1/1 [==============================] - 0s 133ms/step
1/1 [==============================] - 0s 127ms/step
1/1 [==============================] - 0s 130ms/step
1/1 [==============================] - 0s 104ms/step
1/1 [==============================] - 0s 113ms/step
1/1 [==============================] - 0s 126ms/step
1/1 [==============================] - 0s 107ms/step
1/1 [==============================] - 0s 122ms/step
1/1 [==============================] - 0s 130ms/step
1/1 [==============================] - 0s 129ms/step
1/1 [==============================] - 0s 137ms/step
1/1 [==============================] - 0s 133ms/step
1/1 [==============================] - 0s 126ms/step
1/1 [==============================] - 0s 113ms/step
1/1 [==============================] - 0s 107ms/step
1/1 [==============================] - 0s 109ms/step
1/1 [==============================] - 0s 126ms/step
1/1 [==============================] - 0s 142ms/step
1/1 [==============================] - 0s 144ms/step
1/1 [==============================] - 0s 114ms/step
1/1 [==============================] - 0s 162ms/step
1/1 [==============================] - 0s 129ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 98ms/step
1/1 [==============================] - 0s 134ms/step
1/1 [==============================] - 0s 118ms/step
1/1 [==============================] - 0s 151ms/step
1/1 [==============================] - 0s 159ms/step
Encoding image- 200
1/1 [==============================] - 0s 117ms/step
1/1 [==============================] - 0s 119ms/step
1/1 [==============================] - 0s 150ms/step
1/1 [==============================] - 0s 103ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 152ms/step
1/1 [==============================] - 0s 151ms/step
1/1 [==============================] - 0s 121ms/step
1/1 [==============================] - 0s 140ms/step
1/1 [==============================] - 0s 125ms/step
1/1 [==============================] - 0s 115ms/step
1/1 [==============================] - 0s 142ms/step
1/1 [==============================] - 0s 112ms/step
1/1 [==============================] - 0s 120ms/step
1/1 [==============================] - 0s 138ms/step
1/1 [==============================] - 0s 110ms/step
1/1 [==============================] - 0s 104ms/step
1/1 [==============================] - 0s 130ms/step
1/1 [==============================] - 0s 115ms/step
1/1 [==============================] - 0s 117ms/step
1/1 [==============================] - 0s 132ms/step
1/1 [==============================] - 0s 108ms/step
1/1 [==============================] - 0s 134ms/step
1/1 [==============================] - 0s 206ms/step
1/1 [==============================] - 0s 170ms/step
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Encoding image- 400
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Encoding image- 500
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Encoding image- 600
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Encoding image- 800
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Encoding image- 900
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Encoding image- 1000
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Encoding image- 1100
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Encoding image- 1200
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Encoding image- 1300
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Encoding image- 1600
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Encoding image- 1700
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Encoding image- 1900
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Encoding image- 2000
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Encoding image- 2100
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Encoding image- 2200
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Encoding image- 2300
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Encoding image- 4300
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Encoding image- 4400
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Encoding image- 4800
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Encoding image- 4900
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Encoding image- 5700
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Encoding image- 5800
1/1 [==============================] - 0s 126ms/step
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Encoding image- 5900
1/1 [==============================] - 0s 118ms/step
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1/1 [==============================] - 1s 541ms/step
1/1 [==============================] - 0s 167ms/step
1/1 [==============================] - 0s 120ms/step
1/1 [==============================] - 0s 117ms/step
1/1 [==============================] - 0s 105ms/step
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1/1 [==============================] - 0s 137ms/step
1/1 [==============================] - 0s 111ms/step
1/1 [==============================] - 0s 111ms/step
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1/1 [==============================] - 0s 108ms/step
1/1 [==============================] - 0s 95ms/step
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1/1 [==============================] - 0s 221ms/step
1/1 [==============================] - 0s 189ms/step
1/1 [==============================] - 0s 122ms/step
Time taken in seconds = 1133.814924955368
In [38]:
# Save the bottleneck train features to disk

with open("encoded_train_images.pkl", "wb") as encoded_pickle:
    pickle.dump(encoding_train, encoded_pickle)
In [39]:
start = time()

encoding_test = {}

for ix, img in enumerate(test):

    img = "D:/PROGRAMMING/PYTHON/Data Science & Analysis/Pr_4_Image Caption Generator/flickr8k-sau/Flickr_Data/Images/{}.jpg".format(test[ix])
    encoding_test[img[len(images):]] = encode_image(img)
    
    if ix%100==0:
        print("Encoding image- "+ str(ix))
    
print("Time taken in seconds =", time()-start)

1/1 [==============================] - 0s 143ms/step
Encoding image- 0
1/1 [==============================] - 0s 109ms/step
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Encoding image- 100
1/1 [==============================] - 0s 127ms/step
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Encoding image- 300
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Encoding image- 400
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Encoding image- 500
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Encoding image- 600
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Time taken in seconds = 225.61082124710083
In [40]:
# Save the bottleneck train features to disk

with open("encoded_test_images.pkl", "wb") as encoded_pickle:
    pickle.dump(encoding_test, encoded_pickle)
In [41]:
# Load the train images features from disk

with open("encoded_train_images.pkl", "rb") as encoded_pickle:
    encoding_train = pickle.load(encoded_pickle)
In [42]:
# Load the test images features from disk

with open("encoded_test_images.pkl", "rb") as encoded_pickle:
    encoding_test = pickle.load(encoded_pickle)
In [43]:
"""
word_to_idx is mapping between each unique word in all_vocab to int value 
and idx_to_word is vice-versa
"""


ix = 1
word_to_idx = {}
idx_to_word = {}

for e in all_vocab:
    word_to_idx[e] = ix
    idx_to_word[ix] = e
    ix +=1
In [44]:
#  need to add these 2 words as well

word_to_idx['startseq'] = 1846
word_to_idx['endseq'] = 1847

idx_to_word[1846] = 'startseq'
idx_to_word[1847] = 'endseq'
In [45]:
#  vocab_size is total vocabulary len +1 because we will append 0's as well. 

vocab_size = len(idx_to_word)+1
print(vocab_size)

1848
In [46]:
all_captions_len = []

for key in train_descriptions.keys():
    for cap in train_descriptions[key]:
        all_captions_len.append(len(cap.split()))

max_len = max(all_captions_len)
print(max_len)

35
In [47]:
def data_generator(train_descriptions, encoding_train, word_to_idx, max_len, num_photos_per_batch):

    X1, X2, y = [], [], []

    n=0

    while True:
        
        for key, desc_list in train_descriptions.items():
            n +=1

            photo = encoding_train[key+".jpg"]

            for desc in desc_list:
                
                seq = [ word_to_idx[word] for word in desc.split() if word in word_to_idx]  

                for i in range(1,len(seq)):

                    in_seq = seq[0:i]
                    out_seq = seq[i]

                    in_seq = pad_sequences([in_seq], maxlen=max_len, value=0, padding='post')[0]

                    out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]

                    X1.append(photo)
                    X2.append(in_seq)
                    y.append(out_seq)

            if n==num_photos_per_batch:
                yield [[np.array(X1), np.array(X2)], np.array(y)]
                X1, X2, y = [], [], []
                n=0
In [48]:
f = open("glove.6B.50d.txt", encoding='utf8')
In [49]:
embedding_index = {}

for line in f:
    values = line.split()
    word = values[0]
    coefs = np.asarray(values[1:], dtype="float")
    
    embedding_index[word] = coefs
    
f.close()
In [50]:
def get_embedding_output():
    
    emb_dim = 50
    embedding_output = np.zeros((vocab_size,emb_dim))
    
    for word, idx in word_to_idx.items():
        embedding_vector = embedding_index.get(word)
        
        if embedding_vector is not None:
            embedding_output[idx] = embedding_vector
            
    return embedding_output


embedding_output = get_embedding_output()
In [51]:
embedding_output.shape
Out[51]:
(1848, 50)
In [52]:
# image feature extractor model

input_img_fea = Input(shape=(2048,))
inp_img1 = Dropout(0.3)(input_img_fea)
inp_img2 = Dense(256, activation='relu')(inp_img1)
In [53]:
# partial caption sequence model

input_cap = Input(shape=(max_len,))
inp_cap1 = Embedding(input_dim=vocab_size, output_dim=50, mask_zero=True)(input_cap)
inp_cap2 = Dropout(0.3)(inp_cap1)
inp_cap3 = LSTM(256)(inp_cap2)
In [54]:
decoder1 = add([inp_img2 , inp_cap3])
decoder2 = Dense(256, activation='relu')(decoder1)
outputs = Dense(vocab_size, activation='softmax')(decoder2)

# Merge 2 networks
model = Model(inputs=[input_img_fea, input_cap], outputs=outputs)
In [55]:
model.summary()

Model: "model_1"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_3 (InputLayer)           [(None, 35)]         0           []                               
                                                                                                  
 input_2 (InputLayer)           [(None, 2048)]       0           []                               
                                                                                                  
 embedding (Embedding)          (None, 35, 50)       92400       ['input_3[0][0]']                
                                                                                                  
 dropout (Dropout)              (None, 2048)         0           ['input_2[0][0]']                
                                                                                                  
 dropout_1 (Dropout)            (None, 35, 50)       0           ['embedding[0][0]']              
                                                                                                  
 dense (Dense)                  (None, 256)          524544      ['dropout[0][0]']                
                                                                                                  
 lstm (LSTM)                    (None, 256)          314368      ['dropout_1[0][0]']              
                                                                                                  
 add (Add)                      (None, 256)          0           ['dense[0][0]',                  
                                                                  'lstm[0][0]']                   
                                                                                                  
 dense_1 (Dense)                (None, 256)          65792       ['add[0][0]']                    
                                                                                                  
 dense_2 (Dense)                (None, 1848)         474936      ['dense_1[0][0]']                
                                                                                                  
==================================================================================================
Total params: 1,472,040
Trainable params: 1,472,040
Non-trainable params: 0
__________________________________________________________________________________________________
In [56]:
model.layers[2].set_weights([embedding_output])
model.layers[2].trainable = False
In [57]:
model.compile(loss="categorical_crossentropy", optimizer="adam")
In [58]:
epochs = 10
number_pics_per_bath = 3
steps = len(train_descriptions)//number_pics_per_bath
In [59]:
for i in range(epochs):
    generator = data_generator(train_descriptions, encoding_train, word_to_idx, max_len, number_pics_per_bath)
    model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
    model.save('./model_weights/model_' + str(i) + '.h5')

C:\Users\Acer\AppData\Local\Temp\ipykernel_16912\259339223.py:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)

2000/2000 [==============================] - 547s 270ms/step - loss: 4.3289
2000/2000 [==============================] - 442s 221ms/step - loss: 3.6086
2000/2000 [==============================] - 443s 221ms/step - loss: 3.3484
2000/2000 [==============================] - 429s 215ms/step - loss: 3.1910
2000/2000 [==============================] - 435s 217ms/step - loss: 3.0783
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2000/2000 [==============================] - 435s 217ms/step - loss: 2.9281
2000/2000 [==============================] - 428s 214ms/step - loss: 2.8734
2000/2000 [==============================] - 416s 208ms/step - loss: 2.8272
2000/2000 [==============================] - 415s 207ms/step - loss: 2.7881
In [60]:
model = load_model("./model_weights/model_9.h5")
In [61]:
def predict_caption(photo):
    in_text = "startseq"
    
    for i in range(max_len):
        sequence = [word_to_idx[w] for w in in_text.split() if w in word_to_idx]
        sequence = pad_sequences([sequence], maxlen=max_len, padding='post')

        ypred =  model.predict([photo,sequence])
        ypred = ypred.argmax()
        word = idx_to_word[ypred]
        in_text+= ' ' +word
        
        if word =='endseq':
            break
        
        
    final_caption =  in_text.split()
    final_caption = final_caption[1:-1]
    final_caption = ' '.join(final_caption)
    
    return final_caption
In [62]:
for i in range(20):
    rn =  np.random.randint(0, 1000)
    img_name = list(encoding_test.keys())[rn]
    photo = encoding_test[img_name].reshape((1,2048))

    i = plt.imread(images+img_name)
    plt.imshow(i)
    plt.axis("off")
    plt.show()

    caption = predict_caption(photo)
    print(caption)

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man in black shirt and black shorts is riding on street

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man in blue shirt is playing cricket

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dog is jumping up to catch ball

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dog jumps over frisbee

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two dogs are running through the countryside

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boy in blue shirt is jumping in the air

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man in red shirt and khaki shirt is climbing rock face

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man in blue shirt and helmet riding green bike

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man in black shirt is standing in front of traffic

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man and woman are sitting on bench with their bicycles in the background

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two dogs are running through field

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two dogs play in the water

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man with nose and glasses

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two people are standing on top of snowy mountain

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people are sitting on the floor at night

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man in blue shirt and helmet riding his bike in the snow

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boy in blue shirt is playing with toy